kmeansClassify {twoddpcr} | R Documentation

## K-means classify the wells in a `ddpcrWell` or `ddpcrPlate` object, or in a data frame.

### Description

If `droplets` is a data frame, the droplets are classified using the k-means clustering algorithm.

For `ddpcrWell`, the droplets are classified by using the k-means clustering algorithm.

For `ddpcrPlate`, all of the wells are combined and classified, with this new classification assigned to the `ddpcrPlate` object.

### Usage

    kmeansClassify(droplets, centres = matrix(c(0, 0, 10000, 0, 0, 7000, 10000,
      7000), ncol = 2, byrow = TRUE), ...)

    ## S4 method for signature 'data.frame'
    kmeansClassify(droplets, centres = matrix(c(0, 0,
      10000, 0, 0, 7000, 10000, 7000), ncol = 2, byrow = TRUE), fullTable = TRUE)

    ## S4 method for signature 'ddpcrWell'
    kmeansClassify(droplets, centres = matrix(c(0, 0, 10000,
      0, 0, 7000, 10000, 7000), ncol = 2, byrow = TRUE))

    ## S4 method for signature 'ddpcrPlate'
    kmeansClassify(droplets, centres = matrix(c(0, 0,
      10000, 0, 0, 7000, 10000, 7000), ncol = 2, byrow = TRUE))

### Arguments

`droplets` | A `ddpcrWell` or `ddpcrPlate` object, or a data frame with columns `Ch1.Amplitude` and `Ch2.Amplitude`.
`centres` | Either: A matrix corresponding to the initial centres to use for the k-means algorithm; or An integer corresponding to the number of clusters. If this is set, the initial centres are randomly set. Defaults to `matrix(c(0, 0, 10000, 0, 0, 7000, 10000, 7000), ncol=2, byrow=TRUE)`
`...` | Other options depending on the type of `droplets`.
`fullTable` | If `TRUE`, returns a full data frame of droplets and their classification; if `FALSE`, simply returns a factor corresponding to this classification. Defaults to `TRUE`.

### Value

An object with the new classification.

If `droplets` is a data frame, a list is returned with the following components:

`data` | A data frame or vector corresponding to the classification.
`centres` | A data frame listing the final centre points from the k-means algorithm with the corresponding cluster labels.

### Author(s)

Anthony Chiu, anthony.chiu@cruk.manchester.ac.uk

### See Also

This method uses the `kmeans` function.

To manually set and retrieve classifications, use the `wellClassification`, `plateClassification` and `plateClassificationMethod` methods.

For a supervised classification approach, one may want to consider `knnClassify`.

### Examples

    ### Use the KRASdata dataset for all of these examples.

    ## Use K-means clustering to classify droplets into four (the default
    ## number) classes.
    aWell <- kmeansClassify(KRASdata[["E03"]])

    ## We can look the the classification or the centres.
    head(aWell$data)
    aWell$centres

    ## Specify 3 centres for a different sample in KRASdata.
    aWell <- kmeansClassify(KRASdata[["H04"]], centres=3)
    head(aWell$data)

    ## We can be more specific with the choice of centres.
    aWell <- kmeansClassify(KRASdata[["H04"]],
                           centres=matrix(c(5000, 1500, 5500, 7000, 10000,
                                            2000), ncol=2, byrow=TRUE))

    ## We can use \code{ddpcrWell} objects directly as a parameter.
    aWell <- ddpcrWell(well=KRASdata[["E03"]])
    kmeansClassify(aWell)

    ## We can take multiple samples in a \code{ddpcrPlate} object and
    ## classify everything together.
    krasPlate <- ddpcrPlate(wells=KRASdata)
    kmeansClassify(krasPlate)

[Package twoddpcr version 1.8.0 Index]